High-Utility Rare Itemset (HURI) mining finds itemsets from a database which have their utility no less than a given minimum utility threshold and have their support less than a given frequency threshold. Identifying high-utility rare itemsets from a database can help in better business decision making by highlighting the rare itemsets which give high profits so that they can be marketed more to earn good profit. Some two-phase algorithms have been proposed to mine high-utility rare itemsets. The rare itemsets are generated in the first phase and the high-utility rare itemsets are extracted from rare itemsets in the second phase. However, a two-phase solution is inefficient as the number of rare itemsets is enormous as they increase at a very fast rate with the increase in the frequency threshold. In this paper, we propose an algorithm, namely UP-Rare Growth, which uses UP-Tree data structure to find high-utility rare itemsets from a transaction database. Instead of finding the rare itemsets explicitly, our proposed algorithm works on both frequency and utility of itemsets together. We also propose a couple of effective strategies to avoid searching the non-useful branches of the tree. Extensive experiments show that our proposed algorithm outperforms the state-of-the-art algorithms in terms of number of candidates.
Weitere Kapitel dieses Buchs durch Wischen aufrufen
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
- High Utility Rare Itemset Mining over Transaction Databases
- Springer International Publishing
ec4u, Neuer Inhalt/© ITandMEDIA